Time-lapse microgravity application for estimating fluid density changes of multilayer reservoir using DSMVD technique

The fluid density changes parameter in a reservoir during production and injection fluid activities is very interesting to be analyzed. Through this parameter it can be identified the reservoir mass changes and its impact on the effectiveness of production and injection wells in a reservoir. One of the indirect monitoring technologies has been widely used to observe this parameter is a time-lapse microgravity method. For the case of a single-layer reservoir such as carbonate reservoir, the fluid density changes can be estimated easily because the gravity response measured on the surface directly reflects the fluid density changes in the targeted reservoir. This is in contrast to the case of a multilayer reservoir such as sandstone reservoir, the fluid density changes of each layer becomes more difficult to be predicted, because it depends on the processing technique used, also requires the completeness of supporting data such as volume of reservoir fluid production and injection during a period of gravity measurement on the surface. This paper discusses a new technique of a time lapse microgravity method, a DSMVD technique (Deconvolution Simulation of Mass Volume Density), to estimate the fluid density changes of each layer of a multilayer reservoir. Through this technique it can be identified contribution of each layer in conjunction with injection and production activities. The DSMVD technique combines the deconvolution process of time-lapse microgravity anomaly and the simulation technique of fluid movement. As a case study, it has been used the data of time-lapse microgravity in the SS oil field, Central Sumatra-Indonesia, from twice measurements with in a six-month period. This field is a multilayer sandstone reservoir. The depth of the reservoir target is approximately 700 m and the average thickness of each layer is 12 m. By integrating all the processing and modeling results of gravity anomalies supported by data of geological, seismic, wells and other physical properties, it can be derived the information about the reservoir mass changes and its impact on the effectiveness of production and injection wells. This information can provide the significant contribution in the enhanced oil recovery area.